PRISMA diagram



Lifetime + 30 day prevalence (products)



Prevalence by year





LT National + State surveys (U.S and Canada)



Prevalence by year 2013-16





Meta-analysis



##                     proportion           95%-CI %W(fixed) %W(random) byvar
## Florida Health,FYTS     0.0104 [0.0086; 0.0124]       1.8        3.6     9
## Florida Health,FYTS     0.0185 [0.0161; 0.0211]       3.3        3.6     9
## Florida Health,FYTS     0.0361 [0.0329; 0.0396]       6.5        3.7     9
## Florida Health,FYTS     0.0573 [0.0529; 0.0621]       8.3        3.7     9
## Florida Health,FYTS     0.0769 [0.0716; 0.0825]      10.1        3.7    10
## Florida Health,FYTS     0.0984 [0.0918; 0.1053]      10.3        3.7    11
## Florida Health,FYTS     0.1138 [0.1059; 0.1220]       9.3        3.7    12
## Eggers (2017),FYTS      0.0299 [0.0225; 0.0389]       0.8        3.5     9
## Eggers (2017),FYTS      0.0279 [0.0209; 0.0363]       0.8        3.5     9
## Eggers (2017),FYTS      0.0438 [0.0351; 0.0539]       1.2        3.6     9
## Eggers (2017),FYTS      0.0897 [0.0768; 0.1040]       2.2        3.6     9
## Eggers (2017),FYTS      0.1090 [0.0947; 0.1247]       2.6        3.6    10
## Eggers (2017),FYTS      0.1279 [0.1110; 0.1463]       2.4        3.6    11
## Eggers (2017),FYTS      0.1348 [0.1166; 0.1546]       2.3        3.6    12
## CDC, NYTS               0.0325 [0.0266; 0.0392]       1.6        3.6     9
## CDC, NYTS               0.0397 [0.0333; 0.0470]       1.9        3.6     9
## CDC, NYTS               0.0567 [0.0489; 0.0653]       2.6        3.6     9
## CDC, NYTS               0.0762 [0.0666; 0.0868]       2.9        3.6     9
## CDC, NYTS               0.1250 [0.1129; 0.1378]       4.7        3.7    10
## CDC, NYTS               0.1402 [0.1273; 0.1540]       4.9        3.7    11
## CDC, NYTS               0.1481 [0.1349; 0.1622]       5.2        3.7    12
## NIH, PATH               0.0073 [0.0020; 0.0187]       0.1        2.4     9
## NIH, PATH               0.0113 [0.0067; 0.0178]       0.3        3.3     9
## NIH, PATH               0.0401 [0.0316; 0.0501]       1.1        3.6     9
## NIH, PATH               0.0755 [0.0640; 0.0883]       2.0        3.6     9
## NIH, PATH               0.1163 [0.1022; 0.1317]       3.0        3.6    10
## NIH, PATH               0.1690 [0.1519; 0.1872]       3.8        3.6    11
## NIH, PATH               0.1974 [0.1787; 0.2172]       4.1        3.6    12
## 
## Number of studies combined: k = 28
## 
##                      proportion           95%-CI
## Fixed effect model       0.0812 [0.0794; 0.0831]
## Random effects model     0.0618 [0.0483; 0.0788]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.4818 [0.3702; 1.2063]; tau = 0.6941 [0.6084; 1.0983]
##  I^2 = 99.1% [99.0%; 99.2%]; H = 10.53 [9.86; 11.25]
## 
## Test of heterogeneity:
##        Q d.f. p-value
##  2995.95   27       0
## 
## Results for subgroups (fixed effect model):
##              k proportion           95%-CI      Q   I^2
## byvar = 10   4     0.0957 [0.0912; 0.1005]  78.04 96.2%
## byvar = 11   4     0.1215 [0.1160; 0.1272]  84.47 96.4%
## byvar = 12   4     0.1384 [0.1322; 0.1448]  81.81 96.3%
## byvar = 9   16     0.0424 [0.0409; 0.0441] 776.62 98.1%
## 
## Test for subgroup differences (fixed effect model):
##                      Q d.f.  p-value
## Between groups 1975.00    3        0
## Within groups  1020.94   24 < 0.0001
## 
## Results for subgroups (random effects model):
##              k proportion           95%-CI  tau^2    tau
## byvar = 10   4     0.1050 [0.0805; 0.1359] 0.0852 0.2919
## byvar = 11   4     0.1316 [0.1015; 0.1689] 0.0860 0.2932
## byvar = 12   4     0.1459 [0.1134; 0.1858] 0.0837 0.2892
## byvar = 9   16     0.0347 [0.0260; 0.0463] 0.3524 0.5936
## 
## Test for subgroup differences (random effects model):
##                      Q d.f.  p-value
## Between groups   66.53    3 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies



ies.logit <- escalc(xi = w.cases,ni = total, data=group4_LT, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b
##                        proportion           95%-CI %W(fixed) %W(random)
## Florida Health,FYTS        0.0104 [0.0086; 0.0124]       1.8        5.5
## Florida Health,FYTS        0.0185 [0.0161; 0.0211]       3.1        5.6
## Florida Health,FYTS        0.0361 [0.0329; 0.0396]       6.2        5.6
## Florida Health,FYTS        0.0573 [0.0529; 0.0621]       8.0        5.6
## Florida Health,FYTS        0.0769 [0.0716; 0.0825]       9.7        5.6
## Florida Health,FYTS        0.0984 [0.0918; 0.1053]       9.9        5.6
## Florida Health,FYTS        0.1138 [0.1059; 0.1220]       9.0        5.6
## Eggers (2017),FYTS         0.0299 [0.0225; 0.0389]       0.8        5.3
## Eggers (2017),FYTS         0.0279 [0.0209; 0.0363]       0.8        5.3
## Eggers (2017),FYTS         0.0438 [0.0351; 0.0539]       1.2        5.4
## Eggers (2017),FYTS         0.0897 [0.0768; 0.1040]       2.1        5.5
## Eggers (2017),FYTS         0.1090 [0.0947; 0.1247]       2.5        5.6
## Eggers (2017),FYTS         0.1279 [0.1110; 0.1463]       2.3        5.6
## Eggers (2017),FYTS         0.1348 [0.1166; 0.1546]       2.2        5.5
## Trivers (2018),NYTS        0.0890 [0.0851; 0.0930]      24.7        5.7
## Bentivegna (2020),PATH     0.0824 [0.0763; 0.0888]       8.4        5.6
## Morean (2015),             0.0541 [0.0471; 0.0617]       2.9        5.6
## Peters (2018),HHS          0.1048 [0.0944; 0.1160]       4.4        5.6
## 
## Number of studies combined: k = 18
## 
##                      proportion           95%-CI
## Fixed effect model       0.0751 [0.0735; 0.0768]
## Random effects model     0.0607 [0.0475; 0.0771]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.3054 [0.2341; 1.0621]; tau = 0.5526 [0.4839; 1.0306]
##  I^2 = 99.1% [98.9%; 99.2%]; H = 10.46 [9.62; 11.37]
## 
## Test of heterogeneity:
##        Q d.f. p-value
##  1859.31   17       0
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
forest(b,digits = 3,transf= transf.ilogit.int)



Prevalence by year 2017-18





Meta-analysis



##                     proportion           95%-CI %W(fixed) %W(random) byvar
## Florida Health,FYTS     0.0086 [0.0049; 0.0139]       0.1        2.2     9
## Florida Health,FYTS     0.0232 [0.0170; 0.0309]       0.4        2.4     9
## Florida Health,FYTS     0.0365 [0.0284; 0.0460]       0.6        2.4     9
## Florida Health,FYTS     0.0567 [0.0460; 0.0689]       0.8        2.4     9
## Florida Health,FYTS     0.0589 [0.0477; 0.0718]       0.8        2.4    10
## Florida Health,FYTS     0.1066 [0.0912; 0.1236]       1.2        2.5    11
## Florida Health,FYTS     0.1295 [0.1103; 0.1507]       1.1        2.5    12
## Florida Health,FYTS     0.0105 [0.0086; 0.0127]       0.9        2.4     9
## Florida Health,FYTS     0.0212 [0.0184; 0.0243]       1.8        2.5     9
## Florida Health,FYTS     0.0415 [0.0377; 0.0456]       3.6        2.5     9
## Florida Health,FYTS     0.0757 [0.0702; 0.0816]       5.3        2.5     9
## Florida Health,FYTS     0.0962 [0.0896; 0.1030]       5.8        2.5    10
## Florida Health,FYTS     0.1250 [0.1172; 0.1332]       6.6        2.5    11
## Florida Health,FYTS     0.1459 [0.1366; 0.1557]       6.0        2.5    12
## CDC, NYTS               0.0301 [0.0236; 0.0379]       0.6        2.4     9
## CDC, NYTS               0.0350 [0.0280; 0.0431]       0.7        2.4     9
## CDC, NYTS               0.0692 [0.0592; 0.0803]       1.3        2.5     9
## CDC, NYTS               0.1067 [0.0948; 0.1196]       2.1        2.5     9
## CDC, NYTS               0.1437 [0.1302; 0.1580]       2.8        2.5    10
## CDC, NYTS               0.1847 [0.1696; 0.2005]       3.3        2.5    11
## CDC, NYTS               0.2188 [0.2019; 0.2363]       3.5        2.5    12
## CDC, NYTS               0.0289 [0.0228; 0.0361]       0.7        2.4     9
## CDC, NYTS               0.0530 [0.0452; 0.0618]       1.3        2.5     9
## CDC, NYTS               0.0816 [0.0718; 0.0923]       1.9        2.5     9
## CDC, NYTS               0.1455 [0.1326; 0.1592]       3.1        2.5     9
## CDC, NYTS               0.2130 [0.1972; 0.2294]       3.8        2.5    10
## CDC, NYTS               0.2443 [0.2282; 0.2610]       4.5        2.5    11
## CDC, NYTS               0.2771 [0.2595; 0.2953]       4.4        2.5    12
## NIH, PATH               0.0085 [0.0028; 0.0197]       0.0        1.8     9
## NIH, PATH               0.0162 [0.0108; 0.0231]       0.3        2.3     9
## NIH, PATH               0.0369 [0.0296; 0.0454]       0.7        2.4     9
## NIH, PATH               0.0658 [0.0558; 0.0770]       1.2        2.5     9
## NIH, PATH               0.1140 [0.1013; 0.1277]       2.1        2.5    10
## NIH, PATH               0.1445 [0.1301; 0.1599]       2.4        2.5    11
## NIH, PATH               0.1796 [0.1638; 0.1963]       2.9        2.5    12
## Miech (2020),MTF        0.0399 [0.0346; 0.0458]       1.7        2.5     9
## Miech (2020),MTF        0.0980 [0.0894; 0.1071]       3.5        2.5    10
## Miech (2020),MTF        0.1189 [0.1092; 0.1292]       3.8        2.5    12
## Miech (2020),MTF        0.0550 [0.0485; 0.0621]       2.1        2.5     9
## Miech (2020),MTF        0.1420 [0.1321; 0.1523]       5.1        2.5    10
## Miech (2020),MTF        0.1559 [0.1452; 0.1671]       5.1        2.5    12
## 
## Number of studies combined: k = 41
## 
##                      proportion           95%-CI
## Fixed effect model       0.1122 [0.1104; 0.1141]
## Random effects model     0.0731 [0.0594; 0.0897]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.5172 [0.4515; 1.1846]; tau = 0.7192 [0.6720; 1.0884]
##  I^2 = 99.3% [99.2%; 99.4%]; H = 11.84 [11.26; 12.44]
## 
## Test of heterogeneity:
##        Q d.f. p-value
##  5606.20   40       0
## 
## Results for subgroups (fixed effect model):
##              k proportion           95%-CI       Q   I^2
## byvar = 10   7     0.1254 [0.1213; 0.1297]  341.42 98.2%
## byvar = 11   5     0.1613 [0.1555; 0.1673]  245.25 98.4%
## byvar = 12   7     0.1725 [0.1674; 0.1776]  347.29 98.3%
## byvar = 9   22     0.0554 [0.0537; 0.0571] 1460.40 98.6%
## 
## Test for subgroup differences (fixed effect model):
##                      Q d.f. p-value
## Between groups 3211.84    3       0
## Within groups  2394.35   37       0
## 
## Results for subgroups (random effects model):
##              k proportion           95%-CI  tau^2    tau
## byvar = 10   7     0.1172 [0.0902; 0.1511] 0.1520 0.3898
## byvar = 11   5     0.1558 [0.1147; 0.2081] 0.1596 0.3995
## byvar = 12   7     0.1699 [0.1344; 0.2125] 0.1361 0.3690
## byvar = 9   22     0.0386 [0.0293; 0.0506] 0.4474 0.6689
## 
## Test for subgroup differences (random effects model):
##                      Q d.f.  p-value
## Between groups   76.95    3 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies



ies.logit <- escalc(xi = w.cases,ni = total, data=group4_LT, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b
##                     proportion           95%-CI %W(fixed) %W(random)
## Florida Health,FYTS     0.0086 [0.0049; 0.0139]       0.1        2.9
## Florida Health,FYTS     0.0232 [0.0170; 0.0309]       0.4        3.2
## Florida Health,FYTS     0.0365 [0.0284; 0.0460]       0.5        3.3
## Florida Health,FYTS     0.0567 [0.0460; 0.0689]       0.7        3.3
## Florida Health,FYTS     0.0589 [0.0477; 0.0718]       0.7        3.3
## Florida Health,FYTS     0.1066 [0.0912; 0.1236]       1.2        3.4
## Florida Health,FYTS     0.1295 [0.1103; 0.1507]       1.1        3.4
## Florida Health,FYTS     0.0105 [0.0086; 0.0127]       0.9        3.4
## Florida Health,FYTS     0.0212 [0.0184; 0.0243]       1.7        3.4
## Florida Health,FYTS     0.0415 [0.0377; 0.0456]       3.4        3.4
## Florida Health,FYTS     0.0757 [0.0702; 0.0816]       5.0        3.4
## Florida Health,FYTS     0.0962 [0.0896; 0.1030]       5.5        3.4
## Florida Health,FYTS     0.1250 [0.1172; 0.1332]       6.1        3.4
## Florida Health,FYTS     0.1459 [0.1366; 0.1557]       5.6        3.4
## NIH, PATH               0.0085 [0.0028; 0.0197]       0.0        2.1
## NIH, PATH               0.0162 [0.0108; 0.0231]       0.2        3.1
## NIH, PATH               0.0369 [0.0296; 0.0454]       0.7        3.3
## NIH, PATH               0.0658 [0.0558; 0.0770]       1.1        3.4
## NIH, PATH               0.1140 [0.1013; 0.1277]       2.0        3.4
## NIH, PATH               0.1445 [0.1301; 0.1599]       2.3        3.4
## NIH, PATH               0.1796 [0.1638; 0.1963]       2.7        3.4
## Miech (2020),MTF        0.0399 [0.0346; 0.0458]       1.6        3.4
## Miech (2020),MTF        0.0980 [0.0894; 0.1071]       3.3        3.4
## Miech (2020),MTF        0.1189 [0.1092; 0.1292]       3.6        3.4
## Miech (2020),MTF        0.0550 [0.0485; 0.0621]       2.0        3.4
## Miech (2020),MTF        0.1420 [0.1321; 0.1523]       4.8        3.4
## Miech (2020),MTF        0.1559 [0.1452; 0.1671]       4.7        3.4
## Dai (2020),NYTS         0.1110 [0.1064; 0.1157]      14.8        3.5
## Dai (2020),NYTS         0.1470 [0.1422; 0.1520]      21.2        3.5
## Kowitt (2019),NCYTS     0.0959 [0.0853; 0.1074]       2.1        3.4
## 
## Number of studies combined: k = 30
## 
##                      proportion           95%-CI
## Fixed effect model       0.1061 [0.1044; 0.1078]
## Random effects model     0.0652 [0.0537; 0.0790]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.3211 [0.3197; 1.0672]; tau = 0.5667 [0.5654; 1.0330]
##  I^2 = 99.2% [99.1%; 99.3%]; H = 11.00 [10.34; 11.70]
## 
## Test of heterogeneity:
##        Q d.f. p-value
##  3510.47   29       0
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
forest(b,digits = 3,transf= transf.ilogit.int)



Prevalence by year 2019-20





Meta-analysis



##                     proportion           95%-CI %W(fixed) %W(random) byvar
## Florida Health,FYTS     0.0152 [0.0099; 0.0224]       0.2        3.9     9
## Florida Health,FYTS     0.0343 [0.0261; 0.0443]       0.5        4.1     9
## Florida Health,FYTS     0.0663 [0.0551; 0.0788]       1.0        4.1     9
## Florida Health,FYTS     0.1062 [0.0922; 0.1216]       1.5        4.2     9
## Florida Health,FYTS     0.1349 [0.1176; 0.1538]       1.5        4.2    10
## Florida Health,FYTS     0.1646 [0.1454; 0.1853]       1.7        4.2    11
## Florida Health,FYTS     0.2004 [0.1772; 0.2251]       1.6        4.2    12
## Florida Health,FYTS     0.0363 [0.0311; 0.0421]       1.5        4.2     9
## Florida Health,FYTS     0.0824 [0.0748; 0.0904]       3.4        4.2     9
## Florida Health,FYTS     0.1560 [0.1462; 0.1661]       6.2        4.2     9
## Florida Health,FYTS     0.2289 [0.2167; 0.2415]       7.2        4.2     9
## Florida Health,FYTS     0.3114 [0.2976; 0.3254]       8.5        4.2    10
## Florida Health,FYTS     0.3483 [0.3330; 0.3640]       7.6        4.2    11
## Florida Health,FYTS     0.3268 [0.3106; 0.3433]       6.4        4.2    12
## CDC, NYTS               0.0256 [0.0196; 0.0328]       0.5        4.1     9
## CDC, NYTS               0.0555 [0.0465; 0.0655]       1.1        4.1     9
## CDC, NYTS               0.0931 [0.0816; 0.1056]       1.8        4.2     9
## CDC, NYTS               0.1790 [0.1623; 0.1967]       2.6        4.2     9
## CDC, NYTS               0.2787 [0.2585; 0.2996]       3.4        4.2    10
## CDC, NYTS               0.3200 [0.2984; 0.3421]       3.5        4.2    11
## CDC, NYTS               0.3798 [0.3573; 0.4027]       3.9        4.2    12
## Miech (2020),MTF        0.0900 [0.0841; 0.0962]       6.5        4.2     9
## Miech (2020),MTF        0.2180 [0.2096; 0.2267]      14.1        4.2    10
## Miech (2020),MTF        0.2371 [0.2280; 0.2463]      13.8        4.2    12
## 
## Number of studies combined: k = 24
## 
##                      proportion           95%-CI
## Fixed effect model       0.2064 [0.2034; 0.2095]
## Random effects model     0.1339 [0.1048; 0.1696]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.4767 [0.4129; 1.5129]; tau = 0.6904 [0.6426; 1.2300]
##  I^2 = 99.5% [99.5%; 99.6%]; H = 14.54 [13.74; 15.40]
## 
## Test of heterogeneity:
##        Q d.f. p-value
##  4864.62   23       0
## 
## Results for subgroups (fixed effect model):
##              k proportion           95%-CI       Q   I^2
## byvar = 10   4     0.2461 [0.2395; 0.2528]  238.57 98.7%
## byvar = 11   3     0.3111 [0.3000; 0.3223]  154.88 98.7%
## byvar = 12   4     0.2751 [0.2678; 0.2825]  231.19 98.7%
## byvar = 9   13     0.1168 [0.1135; 0.1201] 1536.45 99.2%
## 
## Test for subgroup differences (fixed effect model):
##                      Q d.f. p-value
## Between groups 2703.53    3       0
## Within groups  2161.09   20       0
## 
## Results for subgroups (random effects model):
##              k proportion           95%-CI  tau^2    tau
## byvar = 10   4     0.2289 [0.1730; 0.2964] 0.1250 0.3536
## byvar = 11   3     0.2694 [0.1825; 0.3785] 0.1936 0.4400
## byvar = 12   4     0.2811 [0.2149; 0.3583] 0.1297 0.3602
## byvar = 9   13     0.0718 [0.0503; 0.1013] 0.4733 0.6880
## 
## Test for subgroup differences (random effects model):
##                      Q d.f.  p-value
## Between groups   45.95    3 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies



# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_LT, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b
##                     proportion           95%-CI %W(fixed) %W(random)
## Florida Health,FYTS     0.0152 [0.0099; 0.0224]       0.2        3.7
## Florida Health,FYTS     0.0343 [0.0261; 0.0443]       0.4        3.9
## Florida Health,FYTS     0.0663 [0.0551; 0.0788]       0.8        4.0
## Florida Health,FYTS     0.1062 [0.0922; 0.1216]       1.3        4.0
## Florida Health,FYTS     0.1349 [0.1176; 0.1538]       1.3        4.0
## Florida Health,FYTS     0.1646 [0.1454; 0.1853]       1.4        4.0
## Florida Health,FYTS     0.2004 [0.1772; 0.2251]       1.4        4.0
## Florida Health,FYTS     0.0363 [0.0311; 0.0421]       1.2        4.0
## Florida Health,FYTS     0.0824 [0.0748; 0.0904]       2.8        4.0
## Florida Health,FYTS     0.1560 [0.1462; 0.1661]       5.2        4.0
## Florida Health,FYTS     0.2289 [0.2167; 0.2415]       6.0        4.0
## Florida Health,FYTS     0.3114 [0.2976; 0.3254]       7.1        4.0
## Florida Health,FYTS     0.3483 [0.3330; 0.3640]       6.4        4.0
## Florida Health,FYTS     0.3268 [0.3106; 0.3433]       5.4        4.0
## CDC, NYTS               0.0256 [0.0196; 0.0328]       0.4        3.9
## CDC, NYTS               0.0555 [0.0465; 0.0655]       0.9        4.0
## CDC, NYTS               0.0931 [0.0816; 0.1056]       1.5        4.0
## CDC, NYTS               0.1790 [0.1623; 0.1967]       2.2        4.0
## CDC, NYTS               0.2787 [0.2585; 0.2996]       2.9        4.0
## CDC, NYTS               0.3200 [0.2984; 0.3421]       3.0        4.0
## CDC, NYTS               0.3798 [0.3573; 0.4027]       3.2        4.0
## CDC, NYTS               0.1756 [0.1695; 0.1819]      16.0        4.1
## Miech (2020),MTF        0.0900 [0.0841; 0.0962]       5.5        4.0
## Miech (2020),MTF        0.2180 [0.2096; 0.2267]      11.8        4.1
## Miech (2020),MTF        0.2371 [0.2280; 0.2463]      11.6        4.1
## 
## Number of studies combined: k = 25
## 
##                      proportion           95%-CI
## Fixed effect model       0.2013 [0.1985; 0.2040]
## Random effects model     0.1357 [0.1087; 0.1680]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.4076 [0.3767; 1.3608]; tau = 0.6384 [0.6137; 1.1665]
##  I^2 = 99.5% [99.5%; 99.6%]; H = 14.34 [13.55; 15.17]
## 
## Test of heterogeneity:
##        Q d.f. p-value
##  4934.75   24       0
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
forest(b,digits = 3,transf= transf.ilogit.int)



(REGROUP) 12-m National + State surveys



Prevalence by year 2017-18





Meta-analysis



##                  proportion           95%-CI %W(fixed) %W(random) byvar
## Miech (2020),MTF     0.0299 [0.0253; 0.0351]       7.1       16.5     9
## Miech (2020),MTF     0.0810 [0.0731; 0.0894]      16.4       16.7    10
## Miech (2020),MTF     0.0950 [0.0862; 0.1045]      17.5       16.7    12
## Miech (2020),MTF     0.0441 [0.0382; 0.0505]       9.4       16.6     9
## Miech (2020),MTF     0.1240 [0.1146; 0.1337]      25.4       16.8    10
## Miech (2020),MTF     0.1310 [0.1210; 0.1415]      24.2       16.8    12
## 
## Number of studies combined: k = 6
## 
##                      proportion           95%-CI
## Fixed effect model       0.0925 [0.0889; 0.0962]
## Random effects model     0.0751 [0.0503; 0.1106]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.2803 [0.1298; 2.1247]; tau = 0.5295 [0.3603; 1.4576]
##  I^2 = 98.9% [98.5%; 99.2%]; H = 9.56 [8.12; 11.24]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  456.77    5 < 0.0001
## 
## Results for subgroups (fixed effect model):
##              k proportion           95%-CI     Q   I^2
## byvar = 10   2     0.1051 [0.0989; 0.1116] 44.82 97.8%
## byvar = 12   2     0.1147 [0.1079; 0.1217] 26.50 96.2%
## byvar = 9    2     0.0373 [0.0337; 0.0414] 13.02 92.3%
## 
## Test for subgroup differences (fixed effect model):
##                     Q d.f.  p-value
## Between groups 372.42    2 < 0.0001
## Within groups   84.35    3 < 0.0001
## 
## Results for subgroups (random effects model):
##              k proportion           95%-CI  tau^2    tau
## byvar = 10   2     0.1005 [0.0656; 0.1510] 0.1098 0.3313
## byvar = 12   2     0.1119 [0.0812; 0.1521] 0.0628 0.2505
## byvar = 9    2     0.0364 [0.0249; 0.0530] 0.0744 0.2727
## 
## Test for subgroup differences (random effects model):
##                      Q d.f.  p-value
## Between groups   22.25    2 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies



##                        proportion           95%-CI %W(fixed) %W(random)
## CAMH, OSDUHS               0.0689 [0.0605; 0.0782]       4.5       12.4
## Miech (2020),MTF           0.0299 [0.0253; 0.0351]       3.0       12.3
## Miech (2020),MTF           0.0810 [0.0731; 0.0894]       7.0       12.5
## Miech (2020),MTF           0.0950 [0.0862; 0.1045]       7.5       12.5
## Miech (2020),MTF           0.0441 [0.0382; 0.0505]       4.0       12.4
## Miech (2020),MTF           0.1240 [0.1146; 0.1337]      10.9       12.6
## Miech (2020),MTF           0.1310 [0.1210; 0.1415]      10.4       12.6
## Doggett (2020),COMPASS     0.0572 [0.0551; 0.0593]      52.7       12.7
## 
## Number of studies combined: k = 8
## 
##                      proportion           95%-CI
## Fixed effect model       0.0710 [0.0691; 0.0729]
## Random effects model     0.0718 [0.0523; 0.0979]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.2338 [0.1018; 1.1179]; tau = 0.4835 [0.3191; 1.0573]
##  I^2 = 99.1% [98.8%; 99.3%]; H = 10.38 [9.12; 11.83]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  754.51    7 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies



Prevalence by year 2019-20





Meta-analysis



##                  proportion           95%-CI %W(fixed) %W(random) byvar
## Miech (2020),MTF     0.0700 [0.0647; 0.0756]      16.9       33.3     9
## Miech (2020),MTF     0.1940 [0.1858; 0.2023]      42.2       33.4    10
## Miech (2020),MTF     0.2080 [0.1993; 0.2169]      40.9       33.4    12
## 
## Number of studies combined: k = 3
## 
##                      proportion           95%-CI
## Fixed effect model       0.1700 [0.1653; 0.1749]
## Random effects model     0.1441 [0.0809; 0.2436]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.3277 [0.1089; 16.3914]; tau = 0.5725 [0.3301; 4.0486]
##  I^2 = 99.7% [99.6%; 99.8%]; H = 18.56 [15.79; 21.82]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  689.06    2 < 0.0001
## 
## Results for subgroups (fixed effect model):
##              k proportion           95%-CI    Q I^2
## byvar = 10   1     0.1940 [0.1859; 0.2022] 0.00  --
## byvar = 12   1     0.2080 [0.1994; 0.2169] 0.00  --
## byvar = 9    1     0.0700 [0.0648; 0.0755] 0.00  --
## 
## Test for subgroup differences (fixed effect model):
##                     Q d.f.  p-value
## Between groups 689.06    2 < 0.0001
## Within groups    0.00    0       --
## 
## Results for subgroups (random effects model):
##              k proportion           95%-CI tau^2 tau
## byvar = 10   1     0.1940 [0.1859; 0.2022]    --  --
## byvar = 12   1     0.2080 [0.1994; 0.2169]    --  --
## byvar = 9    1     0.0700 [0.0648; 0.0755]    --  --
## 
## Test for subgroup differences (random effects model):
##                       Q d.f.  p-value
## Between groups   689.06    2 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies




# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_12m, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b
##                  proportion           95%-CI %W(fixed) %W(random)
## CAMH, OSDUHS         0.0999 [0.0914; 0.1090]      11.0       24.9
## Miech (2020),MTF     0.0700 [0.0647; 0.0756]      15.1       25.0
## Miech (2020),MTF     0.1940 [0.1858; 0.2023]      37.5       25.1
## Miech (2020),MTF     0.2080 [0.1993; 0.2169]      36.4       25.1
## 
## Number of studies combined: k = 4
## 
##                      proportion           95%-CI
## Fixed effect model       0.1608 [0.1565; 0.1651]
## Random effects model     0.1318 [0.0803; 0.2087]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.3168 [0.1108; 5.0724]; tau = 0.5628 [0.3329; 2.2522]
##  I^2 = 99.6% [99.5%; 99.7%]; H = 16.60 [14.39; 19.16]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  827.08    3 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
forest(b,digits = 3,transf= transf.ilogit.int)



(REGROUP) 30d National + State surveys



Prevalence by year 2013-16





Meta-analysis



##                     proportion           95%-CI %W(fixed) %W(random) byvar
## Johnson (2016),HKCS     0.0050 [0.0029; 0.0080]      11.3       23.3     9
## Johnson (2016),HKCS     0.0098 [0.0067; 0.0138]      21.2       25.0    10
## Johnson (2016),HKCS     0.0129 [0.0093; 0.0174]      27.7       25.6    11
## Johnson (2016),HKCS     0.0234 [0.0180; 0.0300]      39.8       26.1    12
## 
## Number of studies combined: k = 4
## 
##                      proportion           95%-CI
## Fixed effect model       0.0139 [0.0119; 0.0163]
## Random effects model     0.0113 [0.0062; 0.0204]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.3401 [0.0963; 5.5794]; tau = 0.5832 [0.3104; 2.3621]
##  I^2 = 92.3% [83.5%; 96.4%]; H = 3.60 [2.46; 5.28]
## 
## Test of heterogeneity:
##      Q d.f.  p-value
##  38.98    3 < 0.0001
## 
## Results for subgroups (fixed effect model):
##              k proportion           95%-CI    Q I^2
## byvar = 10   1     0.0098 [0.0069; 0.0138] 0.00  --
## byvar = 11   1     0.0129 [0.0095; 0.0174] 0.00  --
## byvar = 12   1     0.0234 [0.0183; 0.0300] 0.00  --
## byvar = 9    1     0.0050 [0.0031; 0.0080] 0.00  --
## 
## Test for subgroup differences (fixed effect model):
##                    Q d.f.  p-value
## Between groups 38.98    3 < 0.0001
## Within groups   0.00    0       --
## 
## Results for subgroups (random effects model):
##              k proportion           95%-CI tau^2 tau
## byvar = 10   1     0.0098 [0.0069; 0.0138]    --  --
## byvar = 11   1     0.0129 [0.0095; 0.0174]    --  --
## byvar = 12   1     0.0234 [0.0183; 0.0300]    --  --
## byvar = 9    1     0.0050 [0.0031; 0.0080]    --  --
## 
## Test for subgroup differences (random effects model):
##                      Q d.f.  p-value
## Between groups   38.98    3 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies



# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_30d, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b
##                           proportion           95%-CI %W(fixed) %W(random)
## CDPHE,HKCS                    0.0440 [0.0363; 0.0527]      25.3       14.7
## Barrington-Trimis (2020),     0.0060 [0.0034; 0.0097]       3.8       13.6
## Johnson (2016),HKCS           0.0050 [0.0029; 0.0080]       4.0       13.7
## Johnson (2016),HKCS           0.0098 [0.0067; 0.0138]       7.5       14.2
## Johnson (2016),HKCS           0.0129 [0.0093; 0.0174]       9.9       14.4
## Johnson (2016),HKCS           0.0234 [0.0180; 0.0300]      14.2       14.5
## Peters (2018),HHS             0.0491 [0.0418; 0.0572]      35.3       14.8
## 
## Number of studies combined: k = 7
## 
##                      proportion           95%-CI
## Fixed effect model       0.0283 [0.0258; 0.0310]
## Random effects model     0.0157 [0.0084; 0.0290]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.6857 [0.3145; 4.3052]; tau = 0.8281 [0.5608; 2.0749]
##  I^2 = 97.4% [96.1%; 98.2%]; H = 6.17 [5.07; 7.52]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  228.55    6 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
forest(b,digits = 3,transf= transf.ilogit.int)



Prevalence by year 2017-18





Meta-analysis



##                  proportion           95%-CI %W(fixed) %W(random) byvar
## Miech (2020),MTF     0.0160 [0.0127; 0.0200]       6.6       16.3     9
## Miech (2020),MTF     0.0431 [0.0373; 0.0495]      15.6       16.7    10
## Miech (2020),MTF     0.0501 [0.0436; 0.0572]      16.6       16.7    12
## Miech (2020),MTF     0.0260 [0.0215; 0.0311]       9.7       16.5     9
## Miech (2020),MTF     0.0701 [0.0629; 0.0778]      26.1       16.9    10
## Miech (2020),MTF     0.0751 [0.0674; 0.0835]      25.3       16.9    12
## 
## Number of studies combined: k = 6
## 
##                      proportion           95%-CI
## Fixed effect model       0.0517 [0.0490; 0.0546]
## Random effects model     0.0415 [0.0277; 0.0618]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.2683 [0.1219; 2.0404]; tau = 0.5180 [0.3492; 1.4284]
##  I^2 = 98.0% [97.1%; 98.7%]; H = 7.15 [5.87; 8.70]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  255.49    5 < 0.0001
## 
## Results for subgroups (fixed effect model):
##              k proportion           95%-CI     Q   I^2
## byvar = 10   2     0.0585 [0.0538; 0.0636] 30.25 96.7%
## byvar = 12   2     0.0640 [0.0589; 0.0696] 21.82 95.4%
## byvar = 9    2     0.0214 [0.0186; 0.0246] 11.14 91.0%
## 
## Test for subgroup differences (fixed effect model):
##                     Q d.f.  p-value
## Between groups 192.28    2 < 0.0001
## Within groups   63.21    3 < 0.0001
## 
## Results for subgroups (random effects model):
##              k proportion           95%-CI  tau^2    tau
## byvar = 10   2     0.0551 [0.0340; 0.0882] 0.1285 0.3585
## byvar = 12   2     0.0615 [0.0412; 0.0910] 0.0890 0.2983
## byvar = 9    2     0.0205 [0.0127; 0.0328] 0.1109 0.3330
## 
## Test for subgroup differences (random effects model):
##                      Q d.f. p-value
## Between groups   13.67    2  0.0011
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies



# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_30d, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b
##                   proportion           95%-CI %W(fixed) %W(random)
## CDPHE,HKCS            0.0400 [0.0342; 0.0465]       9.9       12.5
## Miech (2020),MTF      0.0160 [0.0127; 0.0200]       4.8       12.2
## Miech (2020),MTF      0.0431 [0.0373; 0.0495]      11.5       12.5
## Miech (2020),MTF      0.0501 [0.0436; 0.0572]      12.2       12.5
## Miech (2020),MTF      0.0260 [0.0215; 0.0311]       7.1       12.4
## Miech (2020),MTF      0.0701 [0.0629; 0.0778]      19.2       12.6
## Miech (2020),MTF      0.0751 [0.0674; 0.0835]      18.6       12.6
## Nguyen (2019),HHS     0.0967 [0.0864; 0.1079]      16.6       12.6
## 
## Number of studies combined: k = 8
## 
##                      proportion           95%-CI
## Fixed effect model       0.0561 [0.0535; 0.0588]
## Random effects model     0.0461 [0.0324; 0.0650]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.2714 [0.1367; 1.3832]; tau = 0.5209 [0.3697; 1.1761]
##  I^2 = 98.1% [97.4%; 98.7%]; H = 7.32 [6.21; 8.62]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  374.94    7 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
forest(b,digits = 3,transf= transf.ilogit.int)



Prevalence by year 2019-20





Meta-analysis



##                  proportion           95%-CI %W(fixed) %W(random) byvar
## Miech (2020),MTF     0.0390 [0.0350; 0.0432]      14.0       33.2     9
## Miech (2020),MTF     0.1260 [0.1192; 0.1330]      42.8       33.4    10
## Miech (2020),MTF     0.1400 [0.1326; 0.1476]      43.1       33.4    12
## 
## Number of studies combined: k = 3
## 
##                      proportion           95%-CI
## Fixed effect model       0.1128 [0.1088; 0.1169]
## Random effects model     0.0897 [0.0478; 0.1620]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.3530 [0.1253; 19.1725]; tau = 0.5942 [0.3540; 4.3786]
##  I^2 = 99.6% [99.4%; 99.7%]; H = 15.85 [13.23; 18.99]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  502.42    2 < 0.0001
## 
## Results for subgroups (fixed effect model):
##              k proportion           95%-CI    Q I^2
## byvar = 10   1     0.1260 [0.1193; 0.1330] 0.00  --
## byvar = 12   1     0.1400 [0.1327; 0.1476] 0.00  --
## byvar = 9    1     0.0390 [0.0351; 0.0432] 0.00  --
## 
## Test for subgroup differences (fixed effect model):
##                     Q d.f.  p-value
## Between groups 502.42    2 < 0.0001
## Within groups    0.00    0       --
## 
## Results for subgroups (random effects model):
##              k proportion           95%-CI tau^2 tau
## byvar = 10   1     0.1260 [0.1193; 0.1330]    --  --
## byvar = 12   1     0.1400 [0.1327; 0.1476]    --  --
## byvar = 9    1     0.0390 [0.0351; 0.0432]    --  --
## 
## Test for subgroup differences (random effects model):
##                       Q d.f.  p-value
## Between groups   502.42    2 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies



# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_30d, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b
##                  proportion           95%-CI %W(fixed) %W(random)
## CDPHE,HKCS           0.0680 [0.0612; 0.0754]      12.0       24.9
## Miech (2020),MTF     0.0390 [0.0350; 0.0432]      12.4       24.9
## Miech (2020),MTF     0.1260 [0.1192; 0.1330]      37.7       25.1
## Miech (2020),MTF     0.1400 [0.1326; 0.1476]      38.0       25.1
## 
## Number of studies combined: k = 4
## 
##                      proportion           95%-CI
## Fixed effect model       0.1063 [0.1027; 0.1100]
## Random effects model     0.0838 [0.0496; 0.1380]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.3244 [0.1194; 5.5102]; tau = 0.5696 [0.3455; 2.3474]
##  I^2 = 99.5% [99.3%; 99.6%]; H = 14.00 [11.91; 16.45]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  587.82    3 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
forest(b,digits = 3,transf= transf.ilogit.int)

LT National + State surveys (U.S)



Prevalence by year 2013-16





Meta-analysis



##                     proportion           95%-CI %W(fixed) %W(random) byvar
## Florida Health,FYTS     0.0104 [0.0086; 0.0124]       1.8        3.6     9
## Florida Health,FYTS     0.0185 [0.0161; 0.0211]       3.3        3.6     9
## Florida Health,FYTS     0.0361 [0.0329; 0.0396]       6.5        3.7     9
## Florida Health,FYTS     0.0573 [0.0529; 0.0621]       8.3        3.7     9
## Florida Health,FYTS     0.0769 [0.0716; 0.0825]      10.1        3.7    10
## Florida Health,FYTS     0.0984 [0.0918; 0.1053]      10.3        3.7    11
## Florida Health,FYTS     0.1138 [0.1059; 0.1220]       9.3        3.7    12
## Eggers (2017),FYTS      0.0299 [0.0225; 0.0389]       0.8        3.5     9
## Eggers (2017),FYTS      0.0279 [0.0209; 0.0363]       0.8        3.5     9
## Eggers (2017),FYTS      0.0438 [0.0351; 0.0539]       1.2        3.6     9
## Eggers (2017),FYTS      0.0897 [0.0768; 0.1040]       2.2        3.6     9
## Eggers (2017),FYTS      0.1090 [0.0947; 0.1247]       2.6        3.6    10
## Eggers (2017),FYTS      0.1279 [0.1110; 0.1463]       2.4        3.6    11
## Eggers (2017),FYTS      0.1348 [0.1166; 0.1546]       2.3        3.6    12
## CDC, NYTS               0.0325 [0.0266; 0.0392]       1.6        3.6     9
## CDC, NYTS               0.0397 [0.0333; 0.0470]       1.9        3.6     9
## CDC, NYTS               0.0567 [0.0489; 0.0653]       2.6        3.6     9
## CDC, NYTS               0.0762 [0.0666; 0.0868]       2.9        3.6     9
## CDC, NYTS               0.1250 [0.1129; 0.1378]       4.7        3.7    10
## CDC, NYTS               0.1402 [0.1273; 0.1540]       4.9        3.7    11
## CDC, NYTS               0.1481 [0.1349; 0.1622]       5.2        3.7    12
## NIH, PATH               0.0073 [0.0020; 0.0187]       0.1        2.4     9
## NIH, PATH               0.0113 [0.0067; 0.0178]       0.3        3.3     9
## NIH, PATH               0.0401 [0.0316; 0.0501]       1.1        3.6     9
## NIH, PATH               0.0755 [0.0640; 0.0883]       2.0        3.6     9
## NIH, PATH               0.1163 [0.1022; 0.1317]       3.0        3.6    10
## NIH, PATH               0.1690 [0.1519; 0.1872]       3.8        3.6    11
## NIH, PATH               0.1974 [0.1787; 0.2172]       4.1        3.6    12
## 
## Number of studies combined: k = 28
## 
##                      proportion           95%-CI
## Fixed effect model       0.0812 [0.0794; 0.0831]
## Random effects model     0.0618 [0.0483; 0.0788]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.4818 [0.3702; 1.2063]; tau = 0.6941 [0.6084; 1.0983]
##  I^2 = 99.1% [99.0%; 99.2%]; H = 10.53 [9.86; 11.25]
## 
## Test of heterogeneity:
##        Q d.f. p-value
##  2995.95   27       0
## 
## Results for subgroups (fixed effect model):
##              k proportion           95%-CI      Q   I^2
## byvar = 10   4     0.0957 [0.0912; 0.1005]  78.04 96.2%
## byvar = 11   4     0.1215 [0.1160; 0.1272]  84.47 96.4%
## byvar = 12   4     0.1384 [0.1322; 0.1448]  81.81 96.3%
## byvar = 9   16     0.0424 [0.0409; 0.0441] 776.62 98.1%
## 
## Test for subgroup differences (fixed effect model):
##                      Q d.f.  p-value
## Between groups 1975.00    3        0
## Within groups  1020.94   24 < 0.0001
## 
## Results for subgroups (random effects model):
##              k proportion           95%-CI  tau^2    tau
## byvar = 10   4     0.1050 [0.0805; 0.1359] 0.0852 0.2919
## byvar = 11   4     0.1316 [0.1015; 0.1689] 0.0860 0.2932
## byvar = 12   4     0.1459 [0.1134; 0.1858] 0.0837 0.2892
## byvar = 9   16     0.0347 [0.0260; 0.0463] 0.3524 0.5936
## 
## Test for subgroup differences (random effects model):
##                      Q d.f.  p-value
## Between groups   66.53    3 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies



ies.logit <- escalc(xi = w.cases,ni = total, data=group4_LT, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b
##                        proportion           95%-CI %W(fixed) %W(random)
## Florida Health,FYTS        0.0104 [0.0086; 0.0124]       1.8        5.5
## Florida Health,FYTS        0.0185 [0.0161; 0.0211]       3.1        5.6
## Florida Health,FYTS        0.0361 [0.0329; 0.0396]       6.2        5.6
## Florida Health,FYTS        0.0573 [0.0529; 0.0621]       8.0        5.6
## Florida Health,FYTS        0.0769 [0.0716; 0.0825]       9.7        5.6
## Florida Health,FYTS        0.0984 [0.0918; 0.1053]       9.9        5.6
## Florida Health,FYTS        0.1138 [0.1059; 0.1220]       9.0        5.6
## Eggers (2017),FYTS         0.0299 [0.0225; 0.0389]       0.8        5.3
## Eggers (2017),FYTS         0.0279 [0.0209; 0.0363]       0.8        5.3
## Eggers (2017),FYTS         0.0438 [0.0351; 0.0539]       1.2        5.4
## Eggers (2017),FYTS         0.0897 [0.0768; 0.1040]       2.1        5.5
## Eggers (2017),FYTS         0.1090 [0.0947; 0.1247]       2.5        5.6
## Eggers (2017),FYTS         0.1279 [0.1110; 0.1463]       2.3        5.6
## Eggers (2017),FYTS         0.1348 [0.1166; 0.1546]       2.2        5.5
## Trivers (2018),NYTS        0.0890 [0.0851; 0.0930]      24.7        5.7
## Bentivegna (2020),PATH     0.0824 [0.0763; 0.0888]       8.4        5.6
## Morean (2015),             0.0541 [0.0471; 0.0617]       2.9        5.6
## Peters (2018),HHS          0.1048 [0.0944; 0.1160]       4.4        5.6
## 
## Number of studies combined: k = 18
## 
##                      proportion           95%-CI
## Fixed effect model       0.0751 [0.0735; 0.0768]
## Random effects model     0.0607 [0.0475; 0.0771]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.3054 [0.2341; 1.0621]; tau = 0.5526 [0.4839; 1.0306]
##  I^2 = 99.1% [98.9%; 99.2%]; H = 10.46 [9.62; 11.37]
## 
## Test of heterogeneity:
##        Q d.f. p-value
##  1859.31   17       0
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
forest(b,digits = 3,transf= transf.ilogit.int)



Prevalence by year 2017-18





Meta-analysis



##                     proportion           95%-CI %W(fixed) %W(random) byvar
## Florida Health,FYTS     0.0086 [0.0049; 0.0139]       0.1        2.2     9
## Florida Health,FYTS     0.0232 [0.0170; 0.0309]       0.4        2.4     9
## Florida Health,FYTS     0.0365 [0.0284; 0.0460]       0.6        2.4     9
## Florida Health,FYTS     0.0567 [0.0460; 0.0689]       0.8        2.4     9
## Florida Health,FYTS     0.0589 [0.0477; 0.0718]       0.8        2.4    10
## Florida Health,FYTS     0.1066 [0.0912; 0.1236]       1.2        2.5    11
## Florida Health,FYTS     0.1295 [0.1103; 0.1507]       1.1        2.5    12
## Florida Health,FYTS     0.0105 [0.0086; 0.0127]       0.9        2.4     9
## Florida Health,FYTS     0.0212 [0.0184; 0.0243]       1.8        2.5     9
## Florida Health,FYTS     0.0415 [0.0377; 0.0456]       3.6        2.5     9
## Florida Health,FYTS     0.0757 [0.0702; 0.0816]       5.3        2.5     9
## Florida Health,FYTS     0.0962 [0.0896; 0.1030]       5.8        2.5    10
## Florida Health,FYTS     0.1250 [0.1172; 0.1332]       6.6        2.5    11
## Florida Health,FYTS     0.1459 [0.1366; 0.1557]       6.0        2.5    12
## CDC, NYTS               0.0301 [0.0236; 0.0379]       0.6        2.4     9
## CDC, NYTS               0.0350 [0.0280; 0.0431]       0.7        2.4     9
## CDC, NYTS               0.0692 [0.0592; 0.0803]       1.3        2.5     9
## CDC, NYTS               0.1067 [0.0948; 0.1196]       2.1        2.5     9
## CDC, NYTS               0.1437 [0.1302; 0.1580]       2.8        2.5    10
## CDC, NYTS               0.1847 [0.1696; 0.2005]       3.3        2.5    11
## CDC, NYTS               0.2188 [0.2019; 0.2363]       3.5        2.5    12
## CDC, NYTS               0.0289 [0.0228; 0.0361]       0.7        2.4     9
## CDC, NYTS               0.0530 [0.0452; 0.0618]       1.3        2.5     9
## CDC, NYTS               0.0816 [0.0718; 0.0923]       1.9        2.5     9
## CDC, NYTS               0.1455 [0.1326; 0.1592]       3.1        2.5     9
## CDC, NYTS               0.2130 [0.1972; 0.2294]       3.8        2.5    10
## CDC, NYTS               0.2443 [0.2282; 0.2610]       4.5        2.5    11
## CDC, NYTS               0.2771 [0.2595; 0.2953]       4.4        2.5    12
## NIH, PATH               0.0085 [0.0028; 0.0197]       0.0        1.8     9
## NIH, PATH               0.0162 [0.0108; 0.0231]       0.3        2.3     9
## NIH, PATH               0.0369 [0.0296; 0.0454]       0.7        2.4     9
## NIH, PATH               0.0658 [0.0558; 0.0770]       1.2        2.5     9
## NIH, PATH               0.1140 [0.1013; 0.1277]       2.1        2.5    10
## NIH, PATH               0.1445 [0.1301; 0.1599]       2.4        2.5    11
## NIH, PATH               0.1796 [0.1638; 0.1963]       2.9        2.5    12
## Miech (2020),MTF        0.0399 [0.0346; 0.0458]       1.7        2.5     9
## Miech (2020),MTF        0.0980 [0.0894; 0.1071]       3.5        2.5    10
## Miech (2020),MTF        0.1189 [0.1092; 0.1292]       3.8        2.5    12
## Miech (2020),MTF        0.0550 [0.0485; 0.0621]       2.1        2.5     9
## Miech (2020),MTF        0.1420 [0.1321; 0.1523]       5.1        2.5    10
## Miech (2020),MTF        0.1559 [0.1452; 0.1671]       5.1        2.5    12
## 
## Number of studies combined: k = 41
## 
##                      proportion           95%-CI
## Fixed effect model       0.1122 [0.1104; 0.1141]
## Random effects model     0.0731 [0.0594; 0.0897]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.5172 [0.4515; 1.1846]; tau = 0.7192 [0.6720; 1.0884]
##  I^2 = 99.3% [99.2%; 99.4%]; H = 11.84 [11.26; 12.44]
## 
## Test of heterogeneity:
##        Q d.f. p-value
##  5606.20   40       0
## 
## Results for subgroups (fixed effect model):
##              k proportion           95%-CI       Q   I^2
## byvar = 10   7     0.1254 [0.1213; 0.1297]  341.42 98.2%
## byvar = 11   5     0.1613 [0.1555; 0.1673]  245.25 98.4%
## byvar = 12   7     0.1725 [0.1674; 0.1776]  347.29 98.3%
## byvar = 9   22     0.0554 [0.0537; 0.0571] 1460.40 98.6%
## 
## Test for subgroup differences (fixed effect model):
##                      Q d.f. p-value
## Between groups 3211.84    3       0
## Within groups  2394.35   37       0
## 
## Results for subgroups (random effects model):
##              k proportion           95%-CI  tau^2    tau
## byvar = 10   7     0.1172 [0.0902; 0.1511] 0.1520 0.3898
## byvar = 11   5     0.1558 [0.1147; 0.2081] 0.1596 0.3995
## byvar = 12   7     0.1699 [0.1344; 0.2125] 0.1361 0.3690
## byvar = 9   22     0.0386 [0.0293; 0.0506] 0.4474 0.6689
## 
## Test for subgroup differences (random effects model):
##                      Q d.f.  p-value
## Between groups   76.95    3 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies



ies.logit <- escalc(xi = w.cases,ni = total, data=group4_LT, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b
##                     proportion           95%-CI %W(fixed) %W(random)
## Florida Health,FYTS     0.0086 [0.0049; 0.0139]       0.1        2.9
## Florida Health,FYTS     0.0232 [0.0170; 0.0309]       0.4        3.2
## Florida Health,FYTS     0.0365 [0.0284; 0.0460]       0.5        3.3
## Florida Health,FYTS     0.0567 [0.0460; 0.0689]       0.7        3.3
## Florida Health,FYTS     0.0589 [0.0477; 0.0718]       0.7        3.3
## Florida Health,FYTS     0.1066 [0.0912; 0.1236]       1.2        3.4
## Florida Health,FYTS     0.1295 [0.1103; 0.1507]       1.1        3.4
## Florida Health,FYTS     0.0105 [0.0086; 0.0127]       0.9        3.4
## Florida Health,FYTS     0.0212 [0.0184; 0.0243]       1.7        3.4
## Florida Health,FYTS     0.0415 [0.0377; 0.0456]       3.4        3.4
## Florida Health,FYTS     0.0757 [0.0702; 0.0816]       5.0        3.4
## Florida Health,FYTS     0.0962 [0.0896; 0.1030]       5.5        3.4
## Florida Health,FYTS     0.1250 [0.1172; 0.1332]       6.1        3.4
## Florida Health,FYTS     0.1459 [0.1366; 0.1557]       5.6        3.4
## NIH, PATH               0.0085 [0.0028; 0.0197]       0.0        2.1
## NIH, PATH               0.0162 [0.0108; 0.0231]       0.2        3.1
## NIH, PATH               0.0369 [0.0296; 0.0454]       0.7        3.3
## NIH, PATH               0.0658 [0.0558; 0.0770]       1.1        3.4
## NIH, PATH               0.1140 [0.1013; 0.1277]       2.0        3.4
## NIH, PATH               0.1445 [0.1301; 0.1599]       2.3        3.4
## NIH, PATH               0.1796 [0.1638; 0.1963]       2.7        3.4
## Miech (2020),MTF        0.0399 [0.0346; 0.0458]       1.6        3.4
## Miech (2020),MTF        0.0980 [0.0894; 0.1071]       3.3        3.4
## Miech (2020),MTF        0.1189 [0.1092; 0.1292]       3.6        3.4
## Miech (2020),MTF        0.0550 [0.0485; 0.0621]       2.0        3.4
## Miech (2020),MTF        0.1420 [0.1321; 0.1523]       4.8        3.4
## Miech (2020),MTF        0.1559 [0.1452; 0.1671]       4.7        3.4
## Dai (2020),NYTS         0.1110 [0.1064; 0.1157]      14.8        3.5
## Dai (2020),NYTS         0.1470 [0.1422; 0.1520]      21.2        3.5
## Kowitt (2019),NCYTS     0.0959 [0.0853; 0.1074]       2.1        3.4
## 
## Number of studies combined: k = 30
## 
##                      proportion           95%-CI
## Fixed effect model       0.1061 [0.1044; 0.1078]
## Random effects model     0.0652 [0.0537; 0.0790]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.3211 [0.3197; 1.0672]; tau = 0.5667 [0.5654; 1.0330]
##  I^2 = 99.2% [99.1%; 99.3%]; H = 11.00 [10.34; 11.70]
## 
## Test of heterogeneity:
##        Q d.f. p-value
##  3510.47   29       0
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
forest(b,digits = 3,transf= transf.ilogit.int)



Prevalence by year 2019-20





Meta-analysis



##                     proportion           95%-CI %W(fixed) %W(random) byvar
## Florida Health,FYTS     0.0152 [0.0099; 0.0224]       0.2        3.9     9
## Florida Health,FYTS     0.0343 [0.0261; 0.0443]       0.5        4.1     9
## Florida Health,FYTS     0.0663 [0.0551; 0.0788]       1.0        4.1     9
## Florida Health,FYTS     0.1062 [0.0922; 0.1216]       1.5        4.2     9
## Florida Health,FYTS     0.1349 [0.1176; 0.1538]       1.5        4.2    10
## Florida Health,FYTS     0.1646 [0.1454; 0.1853]       1.7        4.2    11
## Florida Health,FYTS     0.2004 [0.1772; 0.2251]       1.6        4.2    12
## Florida Health,FYTS     0.0363 [0.0311; 0.0421]       1.5        4.2     9
## Florida Health,FYTS     0.0824 [0.0748; 0.0904]       3.4        4.2     9
## Florida Health,FYTS     0.1560 [0.1462; 0.1661]       6.2        4.2     9
## Florida Health,FYTS     0.2289 [0.2167; 0.2415]       7.2        4.2     9
## Florida Health,FYTS     0.3114 [0.2976; 0.3254]       8.5        4.2    10
## Florida Health,FYTS     0.3483 [0.3330; 0.3640]       7.6        4.2    11
## Florida Health,FYTS     0.3268 [0.3106; 0.3433]       6.4        4.2    12
## CDC, NYTS               0.0256 [0.0196; 0.0328]       0.5        4.1     9
## CDC, NYTS               0.0555 [0.0465; 0.0655]       1.1        4.1     9
## CDC, NYTS               0.0931 [0.0816; 0.1056]       1.8        4.2     9
## CDC, NYTS               0.1790 [0.1623; 0.1967]       2.6        4.2     9
## CDC, NYTS               0.2787 [0.2585; 0.2996]       3.4        4.2    10
## CDC, NYTS               0.3200 [0.2984; 0.3421]       3.5        4.2    11
## CDC, NYTS               0.3798 [0.3573; 0.4027]       3.9        4.2    12
## Miech (2020),MTF        0.0900 [0.0841; 0.0962]       6.5        4.2     9
## Miech (2020),MTF        0.2180 [0.2096; 0.2267]      14.1        4.2    10
## Miech (2020),MTF        0.2371 [0.2280; 0.2463]      13.8        4.2    12
## 
## Number of studies combined: k = 24
## 
##                      proportion           95%-CI
## Fixed effect model       0.2064 [0.2034; 0.2095]
## Random effects model     0.1339 [0.1048; 0.1696]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.4767 [0.4129; 1.5129]; tau = 0.6904 [0.6426; 1.2300]
##  I^2 = 99.5% [99.5%; 99.6%]; H = 14.54 [13.74; 15.40]
## 
## Test of heterogeneity:
##        Q d.f. p-value
##  4864.62   23       0
## 
## Results for subgroups (fixed effect model):
##              k proportion           95%-CI       Q   I^2
## byvar = 10   4     0.2461 [0.2395; 0.2528]  238.57 98.7%
## byvar = 11   3     0.3111 [0.3000; 0.3223]  154.88 98.7%
## byvar = 12   4     0.2751 [0.2678; 0.2825]  231.19 98.7%
## byvar = 9   13     0.1168 [0.1135; 0.1201] 1536.45 99.2%
## 
## Test for subgroup differences (fixed effect model):
##                      Q d.f. p-value
## Between groups 2703.53    3       0
## Within groups  2161.09   20       0
## 
## Results for subgroups (random effects model):
##              k proportion           95%-CI  tau^2    tau
## byvar = 10   4     0.2289 [0.1730; 0.2964] 0.1250 0.3536
## byvar = 11   3     0.2694 [0.1825; 0.3785] 0.1936 0.4400
## byvar = 12   4     0.2811 [0.2149; 0.3583] 0.1297 0.3602
## byvar = 9   13     0.0718 [0.0503; 0.1013] 0.4733 0.6880
## 
## Test for subgroup differences (random effects model):
##                      Q d.f.  p-value
## Between groups   45.95    3 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies



# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_LT, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b
##                     proportion           95%-CI %W(fixed) %W(random)
## Florida Health,FYTS     0.0152 [0.0099; 0.0224]       0.2        3.7
## Florida Health,FYTS     0.0343 [0.0261; 0.0443]       0.4        3.9
## Florida Health,FYTS     0.0663 [0.0551; 0.0788]       0.8        4.0
## Florida Health,FYTS     0.1062 [0.0922; 0.1216]       1.3        4.0
## Florida Health,FYTS     0.1349 [0.1176; 0.1538]       1.3        4.0
## Florida Health,FYTS     0.1646 [0.1454; 0.1853]       1.4        4.0
## Florida Health,FYTS     0.2004 [0.1772; 0.2251]       1.4        4.0
## Florida Health,FYTS     0.0363 [0.0311; 0.0421]       1.2        4.0
## Florida Health,FYTS     0.0824 [0.0748; 0.0904]       2.8        4.0
## Florida Health,FYTS     0.1560 [0.1462; 0.1661]       5.2        4.0
## Florida Health,FYTS     0.2289 [0.2167; 0.2415]       6.0        4.0
## Florida Health,FYTS     0.3114 [0.2976; 0.3254]       7.1        4.0
## Florida Health,FYTS     0.3483 [0.3330; 0.3640]       6.4        4.0
## Florida Health,FYTS     0.3268 [0.3106; 0.3433]       5.4        4.0
## CDC, NYTS               0.0256 [0.0196; 0.0328]       0.4        3.9
## CDC, NYTS               0.0555 [0.0465; 0.0655]       0.9        4.0
## CDC, NYTS               0.0931 [0.0816; 0.1056]       1.5        4.0
## CDC, NYTS               0.1790 [0.1623; 0.1967]       2.2        4.0
## CDC, NYTS               0.2787 [0.2585; 0.2996]       2.9        4.0
## CDC, NYTS               0.3200 [0.2984; 0.3421]       3.0        4.0
## CDC, NYTS               0.3798 [0.3573; 0.4027]       3.2        4.0
## CDC, NYTS               0.1756 [0.1695; 0.1819]      16.0        4.1
## Miech (2020),MTF        0.0900 [0.0841; 0.0962]       5.5        4.0
## Miech (2020),MTF        0.2180 [0.2096; 0.2267]      11.8        4.1
## Miech (2020),MTF        0.2371 [0.2280; 0.2463]      11.6        4.1
## 
## Number of studies combined: k = 25
## 
##                      proportion           95%-CI
## Fixed effect model       0.2013 [0.1985; 0.2040]
## Random effects model     0.1357 [0.1087; 0.1680]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.4076 [0.3767; 1.3608]; tau = 0.6384 [0.6137; 1.1665]
##  I^2 = 99.5% [99.5%; 99.6%]; H = 14.34 [13.55; 15.17]
## 
## Test of heterogeneity:
##        Q d.f. p-value
##  4934.75   24       0
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
forest(b,digits = 3,transf= transf.ilogit.int)



(REGROUP) 12-m National + State surveys



Prevalence by year 2017-18





Meta-analysis



##                  proportion           95%-CI %W(fixed) %W(random) byvar
## Miech (2020),MTF     0.0299 [0.0253; 0.0351]       7.1       16.5     9
## Miech (2020),MTF     0.0810 [0.0731; 0.0894]      16.4       16.7    10
## Miech (2020),MTF     0.0950 [0.0862; 0.1045]      17.5       16.7    12
## Miech (2020),MTF     0.0441 [0.0382; 0.0505]       9.4       16.6     9
## Miech (2020),MTF     0.1240 [0.1146; 0.1337]      25.4       16.8    10
## Miech (2020),MTF     0.1310 [0.1210; 0.1415]      24.2       16.8    12
## 
## Number of studies combined: k = 6
## 
##                      proportion           95%-CI
## Fixed effect model       0.0925 [0.0889; 0.0962]
## Random effects model     0.0751 [0.0503; 0.1106]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.2803 [0.1298; 2.1247]; tau = 0.5295 [0.3603; 1.4576]
##  I^2 = 98.9% [98.5%; 99.2%]; H = 9.56 [8.12; 11.24]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  456.77    5 < 0.0001
## 
## Results for subgroups (fixed effect model):
##              k proportion           95%-CI     Q   I^2
## byvar = 10   2     0.1051 [0.0989; 0.1116] 44.82 97.8%
## byvar = 12   2     0.1147 [0.1079; 0.1217] 26.50 96.2%
## byvar = 9    2     0.0373 [0.0337; 0.0414] 13.02 92.3%
## 
## Test for subgroup differences (fixed effect model):
##                     Q d.f.  p-value
## Between groups 372.42    2 < 0.0001
## Within groups   84.35    3 < 0.0001
## 
## Results for subgroups (random effects model):
##              k proportion           95%-CI  tau^2    tau
## byvar = 10   2     0.1005 [0.0656; 0.1510] 0.1098 0.3313
## byvar = 12   2     0.1119 [0.0812; 0.1521] 0.0628 0.2505
## byvar = 9    2     0.0364 [0.0249; 0.0530] 0.0744 0.2727
## 
## Test for subgroup differences (random effects model):
##                      Q d.f.  p-value
## Between groups   22.25    2 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies



##                        proportion           95%-CI %W(fixed) %W(random)
## Miech (2020),MTF           0.0299 [0.0253; 0.0351]       3.2       14.1
## Miech (2020),MTF           0.0810 [0.0731; 0.0894]       7.4       14.3
## Miech (2020),MTF           0.0950 [0.0862; 0.1045]       7.8       14.3
## Miech (2020),MTF           0.0441 [0.0382; 0.0505]       4.2       14.2
## Miech (2020),MTF           0.1240 [0.1146; 0.1337]      11.4       14.4
## Miech (2020),MTF           0.1310 [0.1210; 0.1415]      10.8       14.4
## Doggett (2020),COMPASS     0.0572 [0.0551; 0.0593]      55.2       14.4
## 
## Number of studies combined: k = 7
## 
##                      proportion           95%-CI
## Fixed effect model       0.0711 [0.0692; 0.0730]
## Random effects model     0.0723 [0.0507; 0.1019]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.2554 [0.1087; 1.4685]; tau = 0.5054 [0.3297; 1.2118]
##  I^2 = 99.2% [99.0%; 99.4%]; H = 11.21 [9.81; 12.81]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  754.30    6 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies



Prevalence by year 2019-20





Meta-analysis



##                  proportion           95%-CI %W(fixed) %W(random) byvar
## Miech (2020),MTF     0.0700 [0.0647; 0.0756]      16.9       33.3     9
## Miech (2020),MTF     0.1940 [0.1858; 0.2023]      42.2       33.4    10
## Miech (2020),MTF     0.2080 [0.1993; 0.2169]      40.9       33.4    12
## 
## Number of studies combined: k = 3
## 
##                      proportion           95%-CI
## Fixed effect model       0.1700 [0.1653; 0.1749]
## Random effects model     0.1441 [0.0809; 0.2436]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.3277 [0.1089; 16.3914]; tau = 0.5725 [0.3301; 4.0486]
##  I^2 = 99.7% [99.6%; 99.8%]; H = 18.56 [15.79; 21.82]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  689.06    2 < 0.0001
## 
## Results for subgroups (fixed effect model):
##              k proportion           95%-CI    Q I^2
## byvar = 10   1     0.1940 [0.1859; 0.2022] 0.00  --
## byvar = 12   1     0.2080 [0.1994; 0.2169] 0.00  --
## byvar = 9    1     0.0700 [0.0648; 0.0755] 0.00  --
## 
## Test for subgroup differences (fixed effect model):
##                     Q d.f.  p-value
## Between groups 689.06    2 < 0.0001
## Within groups    0.00    0       --
## 
## Results for subgroups (random effects model):
##              k proportion           95%-CI tau^2 tau
## byvar = 10   1     0.1940 [0.1859; 0.2022]    --  --
## byvar = 12   1     0.2080 [0.1994; 0.2169]    --  --
## byvar = 9    1     0.0700 [0.0648; 0.0755]    --  --
## 
## Test for subgroup differences (random effects model):
##                       Q d.f.  p-value
## Between groups   689.06    2 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies




# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_12m, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b
##                  proportion           95%-CI %W(fixed) %W(random)
## Miech (2020),MTF     0.0700 [0.0647; 0.0756]      16.9       33.3
## Miech (2020),MTF     0.1940 [0.1858; 0.2023]      42.2       33.4
## Miech (2020),MTF     0.2080 [0.1993; 0.2169]      40.9       33.4
## 
## Number of studies combined: k = 3
## 
##                      proportion           95%-CI
## Fixed effect model       0.1700 [0.1653; 0.1749]
## Random effects model     0.1441 [0.0809; 0.2436]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.3277 [0.1089; 16.3914]; tau = 0.5725 [0.3301; 4.0486]
##  I^2 = 99.7% [99.6%; 99.8%]; H = 18.56 [15.79; 21.82]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  689.06    2 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
forest(b,digits = 3,transf= transf.ilogit.int)



(REGROUP) 30d National + State surveys



Prevalence by year 2013-16





Meta-analysis



##                     proportion           95%-CI %W(fixed) %W(random) byvar
## Johnson (2016),HKCS     0.0050 [0.0029; 0.0080]      11.3       23.3     9
## Johnson (2016),HKCS     0.0098 [0.0067; 0.0138]      21.2       25.0    10
## Johnson (2016),HKCS     0.0129 [0.0093; 0.0174]      27.7       25.6    11
## Johnson (2016),HKCS     0.0234 [0.0180; 0.0300]      39.8       26.1    12
## 
## Number of studies combined: k = 4
## 
##                      proportion           95%-CI
## Fixed effect model       0.0139 [0.0119; 0.0163]
## Random effects model     0.0113 [0.0062; 0.0204]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.3401 [0.0963; 5.5794]; tau = 0.5832 [0.3104; 2.3621]
##  I^2 = 92.3% [83.5%; 96.4%]; H = 3.60 [2.46; 5.28]
## 
## Test of heterogeneity:
##      Q d.f.  p-value
##  38.98    3 < 0.0001
## 
## Results for subgroups (fixed effect model):
##              k proportion           95%-CI    Q I^2
## byvar = 10   1     0.0098 [0.0069; 0.0138] 0.00  --
## byvar = 11   1     0.0129 [0.0095; 0.0174] 0.00  --
## byvar = 12   1     0.0234 [0.0183; 0.0300] 0.00  --
## byvar = 9    1     0.0050 [0.0031; 0.0080] 0.00  --
## 
## Test for subgroup differences (fixed effect model):
##                    Q d.f.  p-value
## Between groups 38.98    3 < 0.0001
## Within groups   0.00    0       --
## 
## Results for subgroups (random effects model):
##              k proportion           95%-CI tau^2 tau
## byvar = 10   1     0.0098 [0.0069; 0.0138]    --  --
## byvar = 11   1     0.0129 [0.0095; 0.0174]    --  --
## byvar = 12   1     0.0234 [0.0183; 0.0300]    --  --
## byvar = 9    1     0.0050 [0.0031; 0.0080]    --  --
## 
## Test for subgroup differences (random effects model):
##                      Q d.f.  p-value
## Between groups   38.98    3 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies



# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_30d, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b
##                           proportion           95%-CI %W(fixed) %W(random)
## CDPHE,HKCS                    0.0440 [0.0363; 0.0527]      25.3       14.7
## Barrington-Trimis (2020),     0.0060 [0.0034; 0.0097]       3.8       13.6
## Johnson (2016),HKCS           0.0050 [0.0029; 0.0080]       4.0       13.7
## Johnson (2016),HKCS           0.0098 [0.0067; 0.0138]       7.5       14.2
## Johnson (2016),HKCS           0.0129 [0.0093; 0.0174]       9.9       14.4
## Johnson (2016),HKCS           0.0234 [0.0180; 0.0300]      14.2       14.5
## Peters (2018),HHS             0.0491 [0.0418; 0.0572]      35.3       14.8
## 
## Number of studies combined: k = 7
## 
##                      proportion           95%-CI
## Fixed effect model       0.0283 [0.0258; 0.0310]
## Random effects model     0.0157 [0.0084; 0.0290]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.6857 [0.3145; 4.3052]; tau = 0.8281 [0.5608; 2.0749]
##  I^2 = 97.4% [96.1%; 98.2%]; H = 6.17 [5.07; 7.52]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  228.55    6 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
forest(b,digits = 3,transf= transf.ilogit.int)



Prevalence by year 2017-18





Meta-analysis



##                  proportion           95%-CI %W(fixed) %W(random) byvar
## Miech (2020),MTF     0.0160 [0.0127; 0.0200]       6.6       16.3     9
## Miech (2020),MTF     0.0431 [0.0373; 0.0495]      15.6       16.7    10
## Miech (2020),MTF     0.0501 [0.0436; 0.0572]      16.6       16.7    12
## Miech (2020),MTF     0.0260 [0.0215; 0.0311]       9.7       16.5     9
## Miech (2020),MTF     0.0701 [0.0629; 0.0778]      26.1       16.9    10
## Miech (2020),MTF     0.0751 [0.0674; 0.0835]      25.3       16.9    12
## 
## Number of studies combined: k = 6
## 
##                      proportion           95%-CI
## Fixed effect model       0.0517 [0.0490; 0.0546]
## Random effects model     0.0415 [0.0277; 0.0618]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.2683 [0.1219; 2.0404]; tau = 0.5180 [0.3492; 1.4284]
##  I^2 = 98.0% [97.1%; 98.7%]; H = 7.15 [5.87; 8.70]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  255.49    5 < 0.0001
## 
## Results for subgroups (fixed effect model):
##              k proportion           95%-CI     Q   I^2
## byvar = 10   2     0.0585 [0.0538; 0.0636] 30.25 96.7%
## byvar = 12   2     0.0640 [0.0589; 0.0696] 21.82 95.4%
## byvar = 9    2     0.0214 [0.0186; 0.0246] 11.14 91.0%
## 
## Test for subgroup differences (fixed effect model):
##                     Q d.f.  p-value
## Between groups 192.28    2 < 0.0001
## Within groups   63.21    3 < 0.0001
## 
## Results for subgroups (random effects model):
##              k proportion           95%-CI  tau^2    tau
## byvar = 10   2     0.0551 [0.0340; 0.0882] 0.1285 0.3585
## byvar = 12   2     0.0615 [0.0412; 0.0910] 0.0890 0.2983
## byvar = 9    2     0.0205 [0.0127; 0.0328] 0.1109 0.3330
## 
## Test for subgroup differences (random effects model):
##                      Q d.f. p-value
## Between groups   13.67    2  0.0011
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies



# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_30d, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b
##                   proportion           95%-CI %W(fixed) %W(random)
## CDPHE,HKCS            0.0400 [0.0342; 0.0465]       9.9       12.5
## Miech (2020),MTF      0.0160 [0.0127; 0.0200]       4.8       12.2
## Miech (2020),MTF      0.0431 [0.0373; 0.0495]      11.5       12.5
## Miech (2020),MTF      0.0501 [0.0436; 0.0572]      12.2       12.5
## Miech (2020),MTF      0.0260 [0.0215; 0.0311]       7.1       12.4
## Miech (2020),MTF      0.0701 [0.0629; 0.0778]      19.2       12.6
## Miech (2020),MTF      0.0751 [0.0674; 0.0835]      18.6       12.6
## Nguyen (2019),HHS     0.0967 [0.0864; 0.1079]      16.6       12.6
## 
## Number of studies combined: k = 8
## 
##                      proportion           95%-CI
## Fixed effect model       0.0561 [0.0535; 0.0588]
## Random effects model     0.0461 [0.0324; 0.0650]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.2714 [0.1367; 1.3832]; tau = 0.5209 [0.3697; 1.1761]
##  I^2 = 98.1% [97.4%; 98.7%]; H = 7.32 [6.21; 8.62]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  374.94    7 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
forest(b,digits = 3,transf= transf.ilogit.int)



Prevalence by year 2019-20





Meta-analysis



##                  proportion           95%-CI %W(fixed) %W(random) byvar
## Miech (2020),MTF     0.0390 [0.0350; 0.0432]      14.0       33.2     9
## Miech (2020),MTF     0.1260 [0.1192; 0.1330]      42.8       33.4    10
## Miech (2020),MTF     0.1400 [0.1326; 0.1476]      43.1       33.4    12
## 
## Number of studies combined: k = 3
## 
##                      proportion           95%-CI
## Fixed effect model       0.1128 [0.1088; 0.1169]
## Random effects model     0.0897 [0.0478; 0.1620]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.3530 [0.1253; 19.1725]; tau = 0.5942 [0.3540; 4.3786]
##  I^2 = 99.6% [99.4%; 99.7%]; H = 15.85 [13.23; 18.99]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  502.42    2 < 0.0001
## 
## Results for subgroups (fixed effect model):
##              k proportion           95%-CI    Q I^2
## byvar = 10   1     0.1260 [0.1193; 0.1330] 0.00  --
## byvar = 12   1     0.1400 [0.1327; 0.1476] 0.00  --
## byvar = 9    1     0.0390 [0.0351; 0.0432] 0.00  --
## 
## Test for subgroup differences (fixed effect model):
##                     Q d.f.  p-value
## Between groups 502.42    2 < 0.0001
## Within groups    0.00    0       --
## 
## Results for subgroups (random effects model):
##              k proportion           95%-CI tau^2 tau
## byvar = 10   1     0.1260 [0.1193; 0.1330]    --  --
## byvar = 12   1     0.1400 [0.1327; 0.1476]    --  --
## byvar = 9    1     0.0390 [0.0351; 0.0432]    --  --
## 
## Test for subgroup differences (random effects model):
##                       Q d.f.  p-value
## Between groups   502.42    2 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies



# logit transformation
ies.logit <- escalc(xi = w.cases,ni = total, data=group4_30d, measure = "PLO")
b<-metaprop(event=w.cases, n=total, studlab=author2, sm="PLO", data=ies.logit, method="Inverse", method.tau="DL", digits = 3)
b
##                  proportion           95%-CI %W(fixed) %W(random)
## CDPHE,HKCS           0.0680 [0.0612; 0.0754]      12.0       24.9
## Miech (2020),MTF     0.0390 [0.0350; 0.0432]      12.4       24.9
## Miech (2020),MTF     0.1260 [0.1192; 0.1330]      37.7       25.1
## Miech (2020),MTF     0.1400 [0.1326; 0.1476]      38.0       25.1
## 
## Number of studies combined: k = 4
## 
##                      proportion           95%-CI
## Fixed effect model       0.1063 [0.1027; 0.1100]
## Random effects model     0.0838 [0.0496; 0.1380]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.3244 [0.1194; 5.5102]; tau = 0.5696 [0.3455; 2.3474]
##  I^2 = 99.5% [99.3%; 99.6%]; H = 14.00 [11.91; 16.45]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  587.82    3 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
forest(b,digits = 3,transf= transf.ilogit.int)